Network provisioning generally relates to resource allocation and management in a network and often includes issues such as admission control and/or dimensioning of the network.
A substantial tendency is that the number of network nodes that form a network is growing fast, resulting in a large, complex network structure and virtual topology. In parallel with this tendency, the traffic volume between the network nodes is also growing continuously and is often difficult to predict. Traffic between nodes is also hard to measure because of the large number of nodes and traffic trunks. Therefore, the calculation of the traffic matrix is very complicated. In most of the cases it is not even possible to calculate the traffic matrix. Furthermore, the degree of traffic changes, requiring reconfiguration of the network, is also very hard to forecast.
The area of resource management includes some currently open problems. To achieve good network utilization, the best possible characterization of the customer's traffic flows is required. Supporting a variety of customer applications typically requires a Service Level Agreement (SLA) between the network provider and the customer, specifying the Quality of Service (QoS) and bandwidth requirements. Currently there are two ways to define the SLA and exercise admission control, namely the trunk-based (customer-pipe) model and the hose-based model, which both are static models.
Admission control is an essential component of any network provisioning architecture, and is generally a question of controlling the number of connections that utilize a given set of resources in a communication network, thereby ensuring that admitted connections have access to the resources that are required to fulfil their Quality of Service (QoS) requirements. On the link level, admission control normally serves to restrict the number of connections simultaneously present on a transport link in the network. This means that new connections may be rejected in order to protect connections that are already admitted for transport over the link.
The issue of connection admission control is generally quite complex, and for networks such as Universal Mobile Telecommunications System (UMTS), Virtual Private Networks (VPNs) and similar communication networks the main problem is to find an efficient admission control strategy that works and at the same time fulfils practical requirements such as limited complexity and high accuracy.
Trunk Model
A simple service model, or traffic provisioning model, for a transport network is to emulate the private line service. This would require a client node to specify the bandwidth requirement between every possible source-destination pair. This model, the trunk model, is depicted in FIG. 1.
When a transport network is based on the trunk model, then a mesh of trunks is created, each trunk extending from one customer endpoint to another. A customer endpoint must maintain a logical interface for each of its trunks. In the context of a UMTS core transport network for example, the customer endpoint is typically a Media Gateway (MGW) or equivalent node.
For the trunk model it is normally expected to give the point-to-point traffic demands. In other words, the specification of the traffic matrix is needed. This model enables the network provider to utilize the network in the best way, since the known traffic matrix and routing strategy exactly determine the required link capacities. On the other hand, a critical part of this model is that the communication pattern between the end-points is very difficult to describe. It is a justified assumption that the network provider has very limited information about the traffic matrix, and the customer is unable to exactly predict and define the loads between the nodes. Consequently, in case of an unknown traffic matrix, the application of the trunk or customer-pipe model is quite limited, especially in the VPN context. Another problem with this model is the significant management complexity. In each node, incoming and outgoing traffic description parameters have to be defined for each associated node to define the required capacities in relation to the associated nodes. In case of a full mesh logical network, the sum of parameters to be configured is proportional to the square of the number N of nodes in the considered network or part of the network.
Hose Model
In the hose model, which is another traffic provisioning model, a customer specifies a set of endpoints to be connected. The connectivity between endpoints in the network is specified by a hose, comprising:                The capacity required for aggregate outgoing traffic from the endpoint into the network (to the other end-points),        The capacity required for aggregate incoming traffic from the network to the endpoint (from the other end-points).        
When transport network dimensioning is based on the hose model, then a customer endpoint maintains just one logical interface, a hose, to the provider access router. FIG. 2 shows an exemplary implementation of a hose using edge-to-edge pipes.
Using the hose model, the traffic matrix is not required, only the incoming and outgoing traffic volumes need to be known for each node. These traffic parameters can be measured or predicted in a more exact way compared to the trunk model. Thus, the hose model is very attractive from the viewpoint of the customer, but it is not so advantageous for the network provider since it implies traffic uncertainty in that the sink nodes are not known. The network must be dimensioned for the worst case traffic distribution, which may cause significant overdimensioning in the network. From the viewpoint of management complexity, the hose model requires significantly less parameters than the trunk model. Since only the aggregated incoming and outgoing traffic volumes have to be defined in each node, the number of parameters to be configured is proportional to the number of nodes in the network.
Measurement Based Admission Control
Yet another type of traffic provisioning model is Measurement Based Admission Control (MBAC). In particular, drop-based MBAC, which is an example of end-to-end measurement based admission control, is known from references [1] and [2]. In MBAC, a node measures the network performance to guide the admission control decisions. For example, the data receiver monitors the user plane packet flows per remote site and detects dropped packets by comparing the Real-time Transfer Protocol (RTP) sequence numbers. A dropped packet is an indication of congestion in the IP backbone and RTP provides a mechanism to detect dropped or out-of-sequence packets. The node admits a new call only if the loss rate towards the remote site in question is below a given threshold value. Given that the queuing delays are likely to be quite small, the quality of service is measured strictly in terms of packet loss.
Admission control decisions in the case of measurement-based provisioning are always based on the actual performance of the network, so all unexpected situations are handled properly. For example, if congestion occurs on a link in multiple-fault situations then packet loss is measured in the data receiver, which blocks further calls until the congestion disappears.
However, measurement-based admission control has the drawback that proper QoS may not be guaranteed if the drop requirement is strict, for example in the backbone network of a UMTS system. This may prevent the use of drop-based MBAC in many applications.
The main features of the prior art traffic provisioning models are summarized in the table below.
Static modelsPropertyTrunkHoseDrop-based MBACConfiguration2 * (N − 1)2 entries per—entries per nodenodeBandwidthhighlowMay be impossibleefficiency forto guarantee properproper QoSQoSprovisioningHandling ofNot supportedNot supportedSupportedunexpectedcongestion
In summary, static admission control can be based on trunk or hose models.
The trunk model, where admission control is based on virtual point-to-point trunks defined in the nodes, enables the network provider to utilize the network in the best way, resulting in more effective network operation. However, the trunk model requires a lot of configuration parameters in all nodes to describe the traffic towards the other sink nodes according to the SLA. The complexity of the trunk configuration grows fast, actually it is proportional to the square of the number of network nodes, and therefore can generally not be used in a large-scale network. If a new node is added to the network, a new trunk must be defined and configured with a capacity limit in all other nodes. Another important problem with the trunk model is that the traffic matrix is not or just partly known. On the other hand, the main advantage of the trunk model is that it offers the best bandwidth efficiency among the available static provisioning methods.
Using the hose model, where admission control in the nodes does not check the destination of the calls, the configuration is much simpler. Only incoming and outgoing traffic description entries need to be configured in the nodes. Furthermore, the hose parameters can be measured or predicted in a simple way. The major drawback of the hose model is that it normally causes significant overdimensioning in the network. Configuration of network nodes in the case of hose model is very simple; in fact only two parameters should be configured in each node. When a new node is added to the system, then the configuration of the other nodes is not affected. However, bandwidth efficiency of the hose model is worse than that of the trunk model.
Therefore, there could be large core networks, large VPNs or other large networks where neither trunk nor hose provisioning is applicable due to management complexity or poor bandwidth efficiency, respectively.